1. Technical Field
The present disclosure relates to image registration, and more particularly, to image registration using mutual information and image registration using a GPU.
2. Discussion of Related Art
Medical imaging is increasingly being used for identification of medical problems for patients. Image registration is a component of post-processing medical images. Image registration typically consists of finding a geometric transformation that aligns a pair of images or volumes. The geometric transformation can be used to compensate for motion, detect changes, or fuse complementary information provided by different acquisition devices. Proposed conventional methods for such transformations range from feature based techniques, which establish correspondences between geometric features extracted from images, to intensity-based approaches.
In intensity-based approaches, image registration typically consists of maximizing an image similarity measure over a set of geometric transformations. In the context of multimodal registration, statistical similarity measures such as Mutual Information (MI) and Normalized Mutual Information (NMI) have proven to be effective image similarity measures. The mutual information of two variables is a quantity that measures the statistical dependence between two random variables.
In the context of image registration, mutual information may be computed from a joint intensity histogram of a pair of images In a typical implementation, image intensities are often uniformly quantized to obtain a joint histogram of a practical size. This strategy does not take into account the non-uniform distribution of intensities often encountered in most medical datasets, and results in a very sparse joint histogram where the joint intensity content has been aggregated in a few histogram bins. For example, in brain images, a disproportionate number of values are located in the first quarter of the joint histogram (e.g., the low intensities) due to the predominance of the background.
The processor of a computer may be used to calculate the joint intensity histogram, derive the mutual information from the calculated histogram, and perform image registration from the derived mutual information. However, such operations are computationally expensive. With these CPU-based implementations, it is typical to use various approximations to maintain reasonable computation times. This may however result in suboptimal alignments.
A CPU can offload calculations and operations associated with graphical rendering to a specialized processor known as a Graphical Processing Unit (GPU). While a GPU is not designed to perform operations related to image registration, workarounds have been developed to cause the GPU to assist or perform image registration. However, conventional workarounds do not make use of a geometry shader, which is available on many modern GPUs, such as the NVIDIA's GeForce 8 architecture.
Thus, there is a need for methods and systems that can more efficiently perform image registration and segmentation when the distribution of intensities in medical datasets is non-uniform, and methods and systems that can more efficiently make use of a GPU to perform image registration.